Cytokine expression patterns: A single-cell RNA sequencing and machine learning based roadmap for cancer classification

What is it about?

Cytokines are small protein molecules that exhibit potent immunoregulatory properties, which are known as the essential components of the tumor immune microenvironment (TIME). While some cytokines are known to be universally upregulated in TIME, the unique cytokine expression patterns have not been fully resolved in specific types of cancers. To address this challenge, we develop a TIME single-cell RNA sequencing (scRNA-seq) dataset, which is designed to study cytokine expression patterns for precise cancer classification. The dataset, including 39 cancers, is constructed by integrating 684 tumor scRNA-seq samples from multiple public repositories. After screening and processing, the dataset retains only the expression data of immune cells. With a machine learning classification model, unique cytokine expression patterns are identified for various cancer categories and pioneering applied to cancer classification with an accuracy rate of 78.01%. Our method will not only boost the understanding of cancer-type-specific immune modulations in TIME but also serve as a crucial reference for future diagnostic and therapeutic research in cancer immunity.

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Photo by SHAMBHAVI SINGH on Unsplash

Photo by SHAMBHAVI SINGH on Unsplash

Why is it important?

Besides malignant cells, there are many other com- ponents in tumors, including stroma cells, immune cells, hormones, cytokines and many other biologically-active molecules, forming a well-organized complex system (Gajewski et al., 2013). TIME consists of the immunological components within tumors, which is known to be associated with tumor development, progression, metastasis and immune escape (Fu et al., 2021). Immune cells in TIME, including monocytes, macrophages, neutrophils, dendritic cells (DCs), natural killer (NK) cells, lymphocytes, and rare progenitors like common myeloid progenitors (CMPs) or hematopoietic stem cells (HSCs), interact with each other and tumor cells through cell signaling molecules, constituting a complicated regulatory network in TIME (Marzagalli et al., 2019, Li et al., 2020a). Identifying the expression patterns of these signaling molecules not only reveals immune cell regula- tions in TIME but also provides essential information for the modeling of the TIME immune network.

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